Lab 5A: Exercises
Bryanna Matias
10/7/2020
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
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## date, intersect, setdiff, uniontime_series_confirmed_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed") ## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications.# Let's get the times series data for deaths
time_series_deaths_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.# Create Keys
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)
# Join tables
time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
# Reformat the data
time_series_long_joined$Date <- mdy(time_series_long_joined$Date)
# Create Report table with counts
time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")# Plot graph to a pdf outputfile
pdf("images/time_series_example_plot.pdf", width=6, height=3)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()## png
## 2# Plot graph to a png outputfile
ppi <- 300
png("images/time_series_example_plot.png", width=6*ppi, height=6*ppi, res=ppi)
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
dev.off()## png
## 2# This is the RMarkdown style for inserting images:
US COVID-19 Deaths
# This is an alternative way using html:
#Interactive Graphs
library("plotly")##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layout#There are two common formats used in graphing that you may come across in examples#Version 1
ggplotly(
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
)#Version 2
US_deaths <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US")
p <- ggplot(data = US_deaths, aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
ggplotly(p)Animated Graphs with gganimate
library("gganimate")
library("transformr")
theme_set(theme_bw())
### An animation of the confirmed cases in select countries
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Confirmed COVID-19 Cases") +
geom_point(aes(group = seq_along(Date))) +
transition_reveal(Date)
# animate(p,renderer = gifski_renderer(), end_pause = 15)
animate(p, end_pause = 15)
Covid-19 Data: 6/13-9/13
Confirmed_State_6_13<- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/06-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)Confirmed_State_9_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)Confirmed_State_9_13 <- Confirmed_State_9_13 %>%
filter(Province_State != "Recovered") Confirmed_State_6_13_9_13_joined <- full_join(Confirmed_State_6_13,
Confirmed_State_9_13, by = c("Province_State"))
head(Confirmed_State_6_13_9_13_joined)## # A tibble: 6 x 5
## # Groups: Province_State [6]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Alabama US 24601 US 138755
## 2 Alaska US 653 US 6268
## 3 Arizona US 34660 US 208512
## 4 Arkansas US 12095 US 70219
## 5 California US 150018 US 761728
## 6 Colorado US 29002 US 61293Confirmed_State_6_13_9_13_joined <- full_join(Confirmed_State_6_13,
Confirmed_State_9_13, by = c("Province_State")) %>%
rename(Confirmed_6_13_2020 = "Confirmed.x", Confirmed_9_13_2020 = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y) %>%
replace_na(list(Confirmed_6_13_2020 = 0))
head(Confirmed_State_6_13_9_13_joined)## # A tibble: 6 x 3
## # Groups: Province_State [6]
## Province_State Confirmed_6_13_2020 Confirmed_9_13_2020
## <chr> <dbl> <dbl>
## 1 Alabama 24601 138755
## 2 Alaska 653 6268
## 3 Arizona 34660 208512
## 4 Arkansas 12095 70219
## 5 California 150018 761728
## 6 Colorado 29002 61293# which(is.na(Confirmed_State_6_13_9_13_joined))
Confirmed_State_6_13_9_13_joined_long <- Confirmed_State_6_13_9_13_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")Challenge 1
#Print a graph (different from the one above) to a png file using 3*ppi for the height and width and display the png file in the report using the above R Markdown format.
ppi <- 300
png("us_confirmed_covid_cases_State_6_13_9_13.png", width=3*ppi, height=3*ppi, res=ppi)
ggplot(Confirmed_State_6_13_9_13_joined_long, aes(x = Confirmed, y = Province_State)) + geom_bar(stat="identity",aes(color = Date)) + labs(title="COVID-19 Confirmed Cases in US",
x ="Number of Confirmed Cases", y = "State/Province in US")## Warning: Removed 1 rows containing missing values (position_stack).dev.off()## png
## 2Challenge 2
#Turn one of the exercises from Lab 5 into an interactive graph with plotyly
ggplotly(ggplot(Confirmed_State_6_13_9_13_joined_long, aes(x = Confirmed, y = Province_State)) + geom_bar(stat="identity",aes(color = Date)) + labs(title="COVID-19 Confirmed Cases in US",
x ="Number of Confirmed Cases", y = "State/Province in US"))## Warning: Removed 1 rows containing missing values (position_stack).Challenge 3
#Create an animated graph of your choosing using the time series data to display an aspect (e.g. states or countries) of the data that is important to you.
data_time <- time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("US","Brazil","India","Italy",
"Mexico","Spain","France","Iran","Peru","UK"))
p <- ggplot(data_time, aes(x = Date, y = Confirmed, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("Top 10 Countries W/ COVID-19 Deaths") +
geom_point(aes(group = seq_along(Date))) + transition_reveal(Date)
# Some people needed to use this line instead
# animate(p,renderer = gifski_renderer(), end_pause = 15)
animate(p, end_pause = 15)